While manually designed features help bring structure to the learning
task through the knowledge they encode, they often suffer problems
of being over-specified, incomplete and laborious to develop. Especially
regarding natural language processing, this limits the robustness
of text mining systems and their ability to generalize across domains,
tasks and languages. By exploiting statistical properties of the data,
features can be learned in an unsupervised fashion instead, which
allows for large-scale training not limited by the scarcity of annotated
data. Such intensively data-driven, deep learning approaches have
in recent years led to numerous breakthroughs in a range of application
domains from computer vision to natural language processing, where
a common theme is the use of unsupervised pre-training to effectively
support supervised learning of deep networks [1]}.
We apply the same idea in modeling bank stress in news, as discussed
in the following.
